High-gain analog beamforming in wireless systems conventionally employs analog phase shifters to coherently combine signals. However, this reliance on active hardware components introduces substantial costs, power consumption, and signal insertion losses, all of which increase with the size of the antenna array. This paper presents a novel framework that replaces phase adjustments with the positional optimization of Movable Antennas (MAs). The core idea is to emulate the function of phase shifters by leveraging the fact that the movement of antennas inherently alter the phase of the wireless channel. To realize this framework, we develop a computationally efficient method, named Derivative Matching Optimization (DMO). Unlike conventional iterative algorithms, DMO is a non-iterative approach that directly determines the optimal MA positions by leveraging a novel geometric detection strategy that decouples the optimization problem. In theoretical analysis, we prove the global optimality of the DMO method under practical MA placement constraints and derive a closed-form expression for the Normalized Mean Square Error (NMSE) to characterize its asymptotic convergence behavior. Simulation results indicate that our DMO-based system substantially outperforms both conventional Fixed-Position Antenna (FPA) arrays and the MA systems that rely on phase shifters, validating its superiority in performance, energy efficiency, and cost-effectiveness.
Fanpo Fu, Haifan Yin, Weidong Li et al.· IEEE Transactions on Communi...· 0 citations
Upper mid-band massive multiple-input multiple-output (MIMO) offers a favorable capacity-coverage trade-off for next-generation wireless systems, but its large antenna arrays, wide bandwidths, and faster temporal variation substantially increase the pilot overhead required for accurate channel state information (CSI) acquisition. To reduce this overhead, this paper establishes a tensor-structured multi-domain channel extrapolation framework that exploits the limited-scattering nature of practical propagation environments to recover complete CSI across the spatial-frequency-temporal (SFT) domains from limited observations. Specifically, we develop a Tucker-based SFT-domain signal model to represent the complete CSI, where the factor matrices are parameterized by angle-delay-Doppler (ADD)-domain grids. Thanks to this representation, we reveal that limited SFT-domain observations imposed by uniform pilot patterns and antenna-port selection inherently induce ADD-domain aliasing, so that multiple physically distinct ADD-domain components become indistinguishable within structured ADD aliasing groups. To tackle this issue, we introduce a support-prior-assisted ADD-domain de-aliasing mechanism that leverages coarse-grained support information. Since exact closed-form characterization of this mechanism is difficult to derive, we propose a tensor-structure-aware axial-attention neural network (TANN), which integrates axis-wise attention with a lightweight multi-scale CNN-based gating module to incorporate support priors for ADD-domain de-aliasing. With tensor-structure modeling and mixed-configuration training over different pilot decimation factors, TANN yields a unified model that generalizes across pilot configurations without retraining. Numerical results demonstrate the effectiveness and strong generalization of the proposed framework over benchmark methods under diverse scenarios.
Jiawei Zhuang, Hongwei Hou, Yafei Wang et al.· 0 citations
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